Duke Researchers Develop Prediction Model to Identify Children With Complex Health Needs At Risk for Hospitalization
An important study led by Duke’s David Ming, MD, and AI Health’s Benjamin Goldstein, PhD, and Nicoleta Economou, PhD, on the use of predictive modeling to identify children with complex health needs who are at high risk for hospitalization, was recently published in Hospital Pediatrics, the official journal of the American, Academy of Pediatrics. The study analyzed data from electronic health records and found that certain demographic, clinical, and health service use factors were associated with a higher risk of future hospitalization. The authors, including Duke’s Richard Chung, MD, and Ursula Rogers, BS, suggest that the use of predictive modeling can help identify children with complex health needs who may benefit from targeted interventions to prevent hospitalizations and improve outcomes. The study is accompanied by a commentary by University of Wisconsin Neil Munjal, MD, MS, titled ‘Machine Learning: Predicting Future Clinical Deterioration in Hospitalized Pediatric Patients,’ which describes the Duke researchers’ machine learning approach as “thought-provoking.”
April Poster Showcase Features Duke’s Successes in Health Data Science
A poster showcase held on Monday, April 2024, 2023 at the Mary Duke Biddle Trent Semans Center for Medical Education featured 28 posters in health data science. This cross-disciplinary event was hosted by multiple organizations, including Duke AI Health, the Laboratory for Transformative Administration, and the Center for Computational Thinking. Poster topics were centered around health data science and covered a wide range of topics including statistics, informatics, machine learning, data engineering, implementation, process engineering, technology development, and applications. The posters were submitted by people at all stages of their careers, including students, trainees, staff, and faculty. Information tables also shared programs and resources relevant to health data science at Duke.
Join us for the Health Data Science Poster Showcase on April 24
The Health Data Science poster showcase will be held in person on Monday, April 24 from 12:00-2:00 PM. We’re excited that this cross-disciplinary event will be hosted by multiple organizations, including Duke AI Health, the Laboratory for Transformative Administration, and the Center for Computational Thinking.
The poster display will take place in the Mary Duke Biddle Trent Semans Center for Medical Education (Trent Semans) on the 6th floor and we’ll serve light refreshments.
More than 25 posters will be presented, including Duke participants from: AI Health Fellowship Program; Biomedical Engineering; Clinical and Translational Science Institute (CTSI); Computer Science; Department of Biostatistics and Bioinformatics; Department of Internal Medicine; Department of Neurosurgery; Department of Surgery; Division of Geriatrics, Department of Medicine; Division of Hematology, Department of Medicine; Duke Clinical Research Institute (DCRI); Electrical and Computer Engineering (ECE); Duke Health Technology Solutions (DHTS); Laboratory for Transformative Administrative (LTA); Master of Management in Clinical Informatics (MMCi); OB-GYN; and Trinity College of Arts & Sciences.
Information tables will include programs from across Duke: The + Programs for Students; Duke AI Health; Biostatistics, Epidemiology, and Research Design (BERD); Center for Computational Thinking; Duke Data Analytics Community; and the Master of Management in Clinical Informatics.
Poster awards will include Best Computational Thinking Poster and the Good DEEDS Award (Ethical and Equitable Data Science).
Please join us! All are welcome, and light refreshments will be served.
Coalition for Health AI Unveils Blueprint for Trustworthy AI in Healthcare
The Coalition for Health AI (CHAI) released its highly anticipated “Blueprint for Trustworthy AI Implementation Guidance and Assurance for Healthcare” (Blueprint). The Blueprint addresses the quickly evolving landscape of health AI tools by outlining specific recommendations to increase trustworthiness within the healthcare community, ensure high-quality care, and meet healthcare needs. The 24-page guide reflects a unified effort among subject matter experts from leading academic medical centers and the healthcare, technology, and other industry sectors, who collaborated under the observation of several federal agencies over the past year.
Call for Participation: Posters for the April 24 Duke Health Data Science Showcase
The Health Data Science poster showcase will be held on Monday, April 24 from 12:00-2:00 PM in-person in the Mary Duke Biddle Trent Semans Center for Medical Education (Trent Semans). We’re excited that this cross-disciplinary event will be hosted by multiple organizations, including Duke AI Health, the Laboratory for Transformative Administration, and the Center for Computational Thinking.
We invite any member of the Duke community to propose a poster entry for participation in this event, including students, trainees, staff, and faculty. This experience is intended to be especially valuable to individuals seeking to gain experience in presenting their work in front of a scientific audience, and the poster itself can become a valuable part of an academic portfolio.
Submit your poster topic at: https://duke.qualtrics.com/jfe/form/SV_1HuvnGKOY4YMa9w
The preferred deadline for poster topics to be submitted is Monday, March 13, 2023 by 11:59 PM (Eastern time).
Update on March 10: We’ve heard from several people that their research is ongoing, and we’ve decided to accept poster topics on an rolling basis, to allow everyone the full opportunity to participate.
Poster topics must be centered around health data science, but can cover a wide range of potential topics, such as statistics, informatics, machine learning, data engineering, implementation, process engineering, technology development, or applications. We especially encourage submissions describing experiences with Duke data sources. Student posters describing class projects (at both the undergraduate and graduate levels) are also encouraged.
After you submit your topic, you’ll then receive a poster template with the correct dimensions.
You’ll need to submit your finalized poster by Friday, April 14 in order to have it printed. If your poster is accepted, the event organizers will print it for you and you will have no cost to participate. The showcase will include poster judging, with recognitions including best poster.
We’re excited to do another poster session after the very successful December event, and we invite you to join us! Please email aihealth@duke.edu if you have any questions.
AI Health Seminar Series Success from 2022
The fall 2022 semester was comprised of 9 AI Health seminars that attracted 463 attendances, including people who attended multiple sessions. Across all of 2022, the AI Health seminar series has hosted 22 virtual seminars with 1,820 cumulative attendances. The AI Health seminar series builds upon the success of our previous “Plus Data Science” learning experiences from 2017 – 2021 under the leadership of Larry Carin, which convened 59 in-person learning experiences and 66 virtual learning experiences. Since its launch in 2018, +DS has held a cumulative 125 learning experience sessions (both in-person and virtual). – Metrics by Tiffany Torres
Algorithms to Assess Stroke Risk are Markedly Worse for Black Americans
Current medical standards for accessing stroke risk perform worse for Black Americans than they do for white Americans, potentially creating a self-perpetuating driver of health inequities. A study, led by Duke Health researchers and appearing online Jan. 24 in the Journal of the American Medical Association, evaluated various existing algorithms and two methods of artificial intelligence assessment that are aimed at predicting a person’s risk of stroke within the next 10 years. The study found that all algorithms were worse at stratifying the risk for people who are Black than people who are white, regardless of the person’s gender. The implications are at the individual and population levels: people at high risk of stroke might not receive treatment, and those at low or no risk are unnecessarily treated.
Highlight Video from Fall Digital Pathology Workshop
Last fall AI Health held an in-person workshop designed to give hands-on experience in working with medical digital pathology images using machine learning. See highlights from the afternoon in a video created by our partners in the Center for Computational Thinking. The concept of “do machine learning in just one afternoon!” was very successful, and we appreciate the participation from all those who attended. We are currently working to design more such studios, and please join our mailing list if you’d like to be notified for upcoming events.
AI Health Seminar: ABCDS Oversight – A framework for the governance and evaluation of algorithms to be deployed at Duke Health
Save the date: February 14, 2023, 12:00 PM EST: Duke AI Health’s Nicoleta Economou, PhD, joins Duke DHTS’s Armando D. Bedoya MD MMCi, to present: ‘Algorithm-Based Clinical Decision Support (ABCDS) Oversight: A framework for the governance and evaluation of algorithms to be deployed at Duke Health.’ During the webinar, which is open to members internal and external to Duke, Drs. Economou and Bedoya will discuss highlights from their recent paper published in the Journal of the American Medical Informatics Association (JAMIA).
Article: Building Better Guardrails for Algorithmic Medicine
Recent years have seen growing interest in the use of artificial intelligence tools for healthcare applications, including diagnosis, risk prediction, clinical decision support, and resource management. Capable of finding hidden patterns within the enormous amounts of data that reside in patient electronic health records (EHRs) and administrative databases, these algorithmic tools are diffusing across the world of patient care. Often, health AI applications are accompanied by assurances of their potential for making medical practice better, safer, and fairer. The reality, however, has turned out to be more complex.
December Poster Showcase Highlights Research of 16 Students and Fellows
Duke AI Health’s HDS research and education hub held a successful Poster Showcase on December 6, 2022, featuring the work of 16 students and fellows. Hosted by Ricardo Henao, PhD, and Shelley Rusincovitch, MMCi, the presenters included members of the HDS fall 2022 student cohort, fellows in the AI Health Data Science Fellowship program, as well as members of AI Health’s Spark Imaging Initiative and Duke Biostatistics & Bioinformatics’s BCTIP program.
Creating a Successful Electronic Health Record (EHR) Study Design Workshop
Congratulations to AI Health Faculty Council member Ben Goldstein, PhD, and Duke Children’s Health & Discovery Initiative Director Jillian Hurst, PhD, for their success in leading the Electronic Health Record (EHR) Study Design Workshop from December 5-9, 2022. The course was offered as a virtual 5-day class providing foundational lectures and hands-on studios on the fundamentals of working with, and designing EHR-based studies. The inaugural workshop generated a great deal of enthusiasm and every seat in the course was filled within 6 weeks of course announcement.
MITRE Grand Challenges Power Hour: Modeling Equitable AI in Digital Health
Join Duke AI Health Director Michael Pencina, PhD, as he takes part in discussions with expert panelists convened from government, industry and academia to discuss recent advances in health AI, including structural biological modeling, computer vision algorithms, and ethical frameworks for employing AI in healthcare. This virtual event, “Modeling Equitable AI in Digital Health,” is hosted by MITRE and will take place starting at 4:00 PM EST on Thursday, December 8, 2022.
REGISTER
Blog: My Cancer on MyChart
DCRI Science and Digital Officer Eric Perakslis, PhD, shares a deeply personal perspective on a recent federal mandate that expands patients’ access to data stored in their EHRs – but also carries its own potential for risks. In his essay, Dr. Perakslis combines the patient and tech expert viewpoints as he surveys the “lumpy, bumpy, imperfect progress” toward better data transparency while undergoing cancer diagnosis and treatment.
Duke AI Health Hosts December EHR Study Design Workshop
Duke AI Health is pleased to announce the Duke Electronic Health Records Study Design Workshop (EHR-SDW) 2022. The workshop will be offered in December as a virtual five-day class that provides foundational lectures and hands-on studios on the fundamentals of working with and designing EHR based studies. The EHR-SDW is targeted toward individuals interested in learning about how to work with and conduct studies using electronic health records (EHR) data. EHR data are a widely available form of real-world data that have become standard in studies ranging from clinical trials, comparative effectiveness, risk prediction, and population health. This workshop is offered through Duke AI Health’s Health Data Science (HDS) program and builds on the success of our highly successful Machine Learning Schools, with 11 events held since 2017.
AI Health Data Science Fellowship Program Welcomes New Members
The AI Health Data Science Fellowship Program is a two-year training program focused on data science with healthcare applications, designed for early-career data scientists with strong backgrounds in quantitative disciplines. Launched in fall of 2019, the program currently has 5 fellows, 2 staff data scientists, and 5 alumni. The program recently came together in-person for lunch for the first time since the pandemic. They gathered to welcome 2 new members: new fellow Angel Huang and new Data Scientist, John Rollman.
AI Health Data Studio: Hands-On Digital Pathology
This in-person workshop presented by Ricardo Henao, PhD; Associate Professor, Department of Biostatistics and Bioinformatics; Chief AI Scientist, Duke AI Health, Akhil Ambekar, MS; Fellow, AI Health Data Science Fellowship Program, with Shelley Rusincovitch, MMCi; Managing Director, Duke AI Health, will give you hands-on experience in working with medical digital pathology images using machine learning. Our use case will be in whole slide images of lymph node sections. We will use the CAMELYON16 dataset (https://camelyon16.grand-challenge.org/), which consists of 400 hematoxylin and eosin-stained whole-slide images. During the workshop, you will learn how to retrieve, manage, and process these images, then apply a machine learning model based on a neural network architecture to classify image regions as normal or malignant. The techniques you learn will also be broadly applicable to other types of medical imaging.
Chief AI Health Scientist Ricard Henao Named Associate Professor
Duke AI Health congratulates Chief AI Health Scientist Ricardo Henao, PhD, on his promotion to the rank of Associate Professor in the Department of Biostatistics and Bioinformatics in the Duke University School of Medicine. Dr. Henao is a major presence in health data science at Duke, where his leadership and expertise in machine learning methods and implementation have made him a sought-after collaborator and instructor. “Dr. Henao is a major asset to Duke AI Health and to the larger Duke community,” said Michael Pencina, PhD, director of Duke AI Health and vice dean for data science at the School of Medicine. “We feel fortunate to be able to benefit from such a rare combination of talent and knowledge spanning research, application, and teaching.”
Duke AI Health Director Michael Pencina Surpasses 100K Citations for Academic Research
Duke AI Health Director and Vice Dean for Data Science Michael J. Pencina, PhD, has achieved a major academic milestone: according to Google Scholar’s analytics, he has recently passed the 100,000 mark for academic citations of his work. Pencina, who in addition to his leadership role in Duke’s efforts to develop, evaluate, and implement ethical and equitable data science, has also worked extensively on the development and evaluation of risk prediction models and clinical trial designs.
Request for Comments: Coalition for Health AI’s White Paper on Bias, Equity, and Fairness
As a member of the Coalition for Health AI, Duke AI Health is working to develop a consensus-driven framework to drive high-quality health care through the adoption of credible, fair, and transparent health AI systems. The coalition is convening a series of virtual workgroup sessions to define core principles and has published a white paper from its first meeting: “Bias, Equity, and Fairness.” Please review the paper and submit your feedback by Sept. 15: https://bit.ly/3wbAXQx. With the help of your ideas, the Coalition for Health AI can advance towards establishing clear and appropriate guidelines and guardrails for the fair, ethical, and useful application of AI and machine learning in health care settings.
Much-Touted Genomic Test Score Shows Minimal Utility in Study Led by AI Health Director Michael Pencina
New research led by Duke AI Health Director Michael Pencina, PhD, published recently in the journal Circulation, looked at the value of using a genomic test to predict the future risk of heart disease. Pencina and colleagues found that the genomic test, referred to as the polygenic risk score (PRS), only marginally added to the predictive information obtained through the assessment of traditional risk factors, concluding that the PRS “had minimal clinical utility”.
Call for Student Applications: AI Health’s Fall 2022 HDS Research Program
We invite Duke students to apply for the Health Data Science (HDS) fall research program. This competitive program, based in Duke AI Health, is designed to allow students who have previous experience in data science to continue their engagement with substantive applied projects. The HDS Research Program offers Duke students, both undergraduate and graduate, the opportunity to be a part of research teams applying advanced machine learning (deep learning) to important areas of medicine. Participating students will be mentored by leading Duke faculty involved in data science research, often with guidance by practicing clinicians. The fall will culminate in a showcase session where student teams will present their results.
Duke Authors Introduce Framework for Clinical Algorithm Oversight
A group of Duke Health researchers recently shared their insights on approaches to managing the complex issues that are emerging as “algorithmic medicine” increasingly becomes part of clinical care at hospitals and health systems. The authors, who comprise faculty and staff from Duke AI Health, Duke Health Technology Solutions, the Duke Institute for Health Innovation, and other physicians and researchers from Duke University and Duke University Health System, published an account of their approach to evaluating and monitoring the use of algorithmic predictive models at Duke Health hospitals and clinics. The article, titled “A framework for the oversight and local deployment of safe and high-quality prediction models,” was published on May 31 in the Journal of the American Medical Informatics Association (JAMIA). It showcases the processes and procedures by which an expert group at Duke Health known as Algorithm-Based Clinical Decision Support (ABCDS) Oversight reviews, approves, and manages predictive models intended for use in patient care settings.
Duke AI Health Spark Seminar Series: Medical Imaging AI – Where do we go from here?
Can AI safely automate medical decision-making tasks to improve patient outcomes? In this talk, the presenters will share the challenges in the development and translation of medical AI, and how they are being addressed through a blend of innovation in algorithm development, dataset curation, and implementation design. They will first talk about self-supervised learning methods for medical image classification that leverage large unlabeled datasets to reduce the number of manual annotations required for expert-level performance. Then, they will discuss open benchmarks that can help the community transparently measure advancements in generalizability of algorithms to new geographies, patient populations, and clinical settings. Third, they will share insights from studies that investigate how to optimize human-AI collaboration in the context of clinical workflows and deployment settings. Altogether, this talk will cover key ways in which we can realize the potential of medical AI to make healthcare more accurate, efficient and accessible for patients worldwide.
Duke Machine Learning Summer School 2022
The Duke+Data Science program is pleased to announce the Duke Machine Learning Summer School 2022, offered in June as a live five-day class that provides lectures on the fundamentals of machine learning. The curriculum in the MLSS is targeted to individuals interested in learning about machine learning, with a focus on recent deep learning methodology. The MLSS will introduce the mathematics and statistics at the foundation of modern machine learning, and provide context for the methods that have formed the foundations of rapid growth in artificial intelligence (AI).
Call for Student Applications: HDS Summer 2022 Research Program
We invite Duke students to apply for the Health Data Science (HDS) summer research program. This competitive program, based in Duke AI Health, is designed to allow students who have previous experience in data science to continue their engagement with substantive applied projects. The Advanced Machine Learning Projects in Health Data Science offers Duke students, both undergraduate and graduate, the opportunity to be a part of research teams applying advanced machine learning (deep learning) to important areas of medicine. Participating students will be mentored by leading Duke faculty involved in data science research, often with guidance by practicing clinicians. The summer will culminate in a showcase session where student teams will present their results.
Maciej Mazurowski Joins Duke AI Health to Coordinate New Medical Imaging Initiative
Duke AI Health welcomes Maciej Mazurowski, PhD, who will join its Faculty Council as Director of Radiology Imaging. At AI Health, Dr. Mazurowski will coordinate the AI Health Initiative for Medical Imaging. This new effort will engage experts in machine learning and clinical medicine from across Duke’s campus to foster and accelerate the development, validation, and clinical implementation of machine learning algorithms for medical imaging. “I’m excited to undertake this new challenge and I’m looking forward to working with experts and leadership across the entire campus to build on existing technical and clinical strengths in medical imaging AI at Duke,” Dr. Mazurowski said.
2022 Spring AI Health Proposal Studios
The mission of Duke AI Health is to enable the discovery, development, and implementation of artificial intelligence (AI) at Duke and beyond. A key component to achieving this goal is to foster high-impact, rigorous, and competitive proposals for scientific awards. The 2022 AI Health Proposal Studios will provide a structured opportunity for investigators to engage with Duke’s top data science expertise and thought leadership, and to receive review and feedback of the scientific components of their proposals. After seeing a strong response to the Proposal Studio concept and the following virtual learning experiences in 2021, AI Health plans to continue building on last year’s success with the overarching goal of fostering high-impact, rigorous, and competitive proposals for scientific awards.
Duke Health Data Analytics Community Praised for Commitment to Data Democracy
In a recent post at the Tableau blog, the data visualization company praises the Duke Analytics Community (DAC) for the group’s commitment to “taking data democracy to (the) next level.” The post, which is available at the Tableau website, singled out the Duke Cancer Institute’s Claire Howell and Duke University’s Rebecca McDaniel for recognition based on their initiative in helping to create a “department-agnostic space” where users of analytics software across the School of Medicine and Health System could share ideas and improve data access.
Duke School of Nursing’s Michael Cary Selected as Inaugural AI Health Equity Scholar
Duke AI Health welcomes its first AI Health Equity Scholar, Michael P. Cary, PhD, RN, who is now beginning a yearlong scholarship supported by Duke AI Health and the Duke Clinical & Translational Science Institute. The AI Health Equity Scholars Program, which provides funding for Duke University faculty, staff, and postdoctoral scholars to actively collaborate with AI Health leadership, is focused on broadening Duke’s commitment to ethical and equitable data science and artificial intelligence (AI) in health applications.
Announcing the Spring 2022 AI Health Data Studio Seminars
Duke AI Health is pleased to launch the AI Health Data Studio Seminar series this spring. This multi-part educational offering is designed for campus-based researchers at Duke who are interested in working with medical data but are unsure where to begin. Hosted by Senior Informacist Ursula Rogers, Chief AI Health Scientist Ricardo Henao, PhD, and Associate Director of Informatics Shelley Rusincovitch, MMCi, the series will feature data experts from across the Duke enterprise.Campus-based researchers are especially invited to attend along with anyone interested from the Duke community, including faculty, staff, and students.
Call for Applications: The AI Health Equity Scholars Program
Duke AI Health and the Duke Clinical & Translational Science Institute are pleased to announce a call for applications with the AI Health Equity Scholars Program. This program will support a minimum 1-year appointment for a faculty member, staff member, or postdoctoral scholar at Duke University. The AI Health Equity Scholars Program is a new initiative intended to broaden our commitment to ethical and equitable data science and artificial health (AI) applications, with direction from CTSI Director L. Ebony Boulware, MD, MHS, and AI Health Director Michael J. Pencina, PhD. The intention of this program is to broaden our expertise in considering and applying ethical and equitable principles for key initiatives within Duke AI Health. Applications must be submitted by Friday, December 10, 2021 by 10 PM (Eastern Time).
Vice Dean for Data Science Michael Pencina, PhD, takes the role of Director for Duke AI Health
Given the rapid growth in and importance of harnessing health data as a tool, Mary Klotman, MD, Dean, Duke University School of Medicine, recently announced the key leadership appointment of Michael Pencina, PhD, Vice Dean for Data Science for the School of Medicine, as the Director of Duke AI Health effective October 13, 2021. Designed as a multidisciplinary initiative, AI Health intends to unlock the enormous opportunity to spur collaborations that will leverage knowledge and expertise from across campus.
Call for Applications: Clinical Research with Electronic Health Records Data (CR-EHR) Course
Duke AI Health and the Duke Clinical & Translational Sciences Institute are pleased to announce a call for applications to the Spring 2022 Clinical Research with Electronic Health Records Data (CR-EHR) Course, with a November 19, 2021 application submission deadline. CR-EHR is an interdisciplinary course designed to engage both clinical and quantitative researchers in learning how to access and work with Duke EHR data. Data captured in the Duke EHR represent the broad spectrum of patient care delivered by Duke Health, which can be leveraged for a variety of research questions and study designs. Clinical trainees will develop a deeper understanding of the types of analytic studies that can be conducted with EHR data, while quantitative trainees will develop a deeper knowledge base for how to query and process EHR data.
BLOG: Using Medical Records to Prepare Doctors and Patients for Difficult Conversations
Now more than ever, clinicians can access an incredible amount of data about their patients. Electronic health records (EHRs) offer a massive repository of information about each individual: notes of all kinds, laboratory results, imaging data, scanned forms, and saved images. Soon, we may even be able to add data from wearable devices such as personal fitness trackers into the mix. However, this breadth of information can be both a blessing and a curse. Clinicians can learn more about their patients from the medical chart than was previously possible—but only if they are able to rapidly and accurately sort through that information and find the most relevant points for a given clinical encounter.
Duke AI Health Director Pencina Joins Expert Panel for Discussion on AI Ethics
This December, Duke AI Health Director and Professor of Biostatistics and Bioinformatics Michael Pencina will join a group of experts for a panel discussion hosted by the Duke Alumni Forever Learning Institute called “Artificial Intelligence: Capabilities, Liabilities, and Responsibilities.” The discussion, the final installment in a four-part series taking place this fall titled “Artificial Intelligence: Real Ethical Quandaries,” will focus on the expanding role of artificial intelligence in decision-making and the practical and ethical issues that can arise from the use of a technology whose inner workings are often opaque and whose operations can be affected by built-in biases. Panel participants will examine how these technologies are being used in arenas such as medicine and national security and discuss the potential impacts of these tools, both positive and negative, on people’s daily lives. The session will take place as an online Zoom webinar on Tuesday, December 7, 2021, from 3:00-4:00 PM Eastern time, and will be moderated by Duke Law Professor and Director of the Duke Initiative for Science and Society Nita Farahany.
“We Should Agree That Hackers Are the Immune System of the Internet”
Eric Perakslis, PhD, DCRI’s Chief Science & Digital Officer, will present at DEF CON 2021 in a talk called “Truth, Trust, and Biodefense.” Learn more about his presentation in his blog post for the DCRI below:
“On May 12, 2017, a ransomware cyberattack known as WannaCry was launched. Within a day, it was raging worldwide and had infected tens of thousands of computers and electronic devices belonging to the United Kingdom’s National Health Service, causing severe disruptions to hospital operations. Shortly after 15:00 UTC on May 13, the infection was halted when information security researcher and hacker Marcus Hutchins discovered and exploited a “kill switch” embedded in the malware’s code. In addition to greatly slowing WannaCry’s spread, this kill switch also prevented infected computers from being encrypted and their data locked. Marcus Hutchins’ story is notably complex, but there is no denying that his actions greatly decreased the global harm that likely would have otherwise occurred. The term hacker often brings to mind a faceless, hooded figure that is ubiquitously linked to crime. Given how pervasive this image is, it may surprise some to learn that there are many “good” hackers. This distinction is made especially clear in the viral TED Talk given by cybersecurity Keren Elazari titled “Hackers: the internet’s immune system.” In this talk, Elazari argues that hackers make the internet stronger by testing its defenses, which forces the internet to adapt, improve, and strengthen, not unlike the body’s adaptive immune system.
Duke AI Health Director Pencina Quoted in Article Spotlighting Predictive Clinical Algorithms
Duke AI Health Director Michael Pencina, PhD, who is a professor of biostatistics and bioinformatics at Duke and serves as the medical school’s vice dean for data science and information technology, was recently quoted in an article appearing in STAT News examining the use of commercially developed predictive algorithms in medicine. In an investigative report for STAT News, correspondent Casey Ross spoke with employees in multiple health systems across the country that use clinical algorithms created by Epic, one of the nation’s largest electronic health record vendors.
Duke +Data Science End of Semester Wrap-up, Spring 2021
Duke+DataScience (+DS) is a Duke-wide educational initiative devoted to expanding knowledge of and facility with machine learning and other artificial intelligence tools across multiple academic fields, including the arts, humanities, and social sciences as well as medicine and quantitative sciences. With an extensive and growing curriculum that includes both online and in-person courses in neural networks, natural language processing, deep learning, and other machine learning applications, +DS offerings span learning needs ranging from novice to expert and are tailored to specific academic and professional applications.
Duke Machine Learning School Concludes Summer 2021 Virtual Offering
Duke’s +Data Science (+DS) recently concluded its 2021 Machine Learning Virtual Summer School (MLvSS). This event, the ninth machine learning school held since 2017, sold out more than a month in advance and completely filled a 100-person waitlist. This high demand reflects both the substantial demand for instruction in methods driving the rapid growth in artificial intelligence, as well as a keen interest in tapping into high-quality instruction from Duke teachers with expertise in the mathematics and statistics that underlie modern machine learning methods.
Duke AI Health and +Data Science Launch Successful Proposal Studio Series
Keeping up with the pace of research in health data science is challenging at the best of times, and the COVID-19 pandemic has not made things any easier. For this reason, Duke AI Health and the Duke +Data Science (+DS) program worked together this spring to launch the Proposal Studio Virtual Learning Experiences (vLE). The Proposal Studios sessions were designed to help investigators develop effective, successful proposals for research project involving health data science. From March through April of 2021, +DS held four successful proposal studios, assisting 13 investigators to develop scientific proposals. Open to anyone within the Duke community, the series attracted a total of 129 attendees and averaged 32 audience members per vLE.
Duke Rheumatologists Explore the Effects of a Rapid Transition to Telemedicine During the COVID-19 Pandemic
The COVID-19 pandemic has prompted a surge in demand for telehealth services, but many questions about how healthcare providers can adapt their practice to meet the challenges of telemedicine remain to be answered. Now, a group of rheumatologists at Duke University School of Medicine have used data drawn from the Duke University Health System’s EHRs (electronic health records) to investigate how a rapid transition to telemedicine affected their approach to patient care.
Learning to DANNCE
A group of neuroscientists and machine learning experts are developing new ways to analyze animal movement and behavior to gain insights into the inner workings of the nervous system. Combining expertise from the disciplines of neurobiology and artificial intelligence, a team of researchers from Duke University, Harvard, MIT, Rockefeller University, and Columbia University have developed a system that captures detailed, multiple-view video of animals in their natural environment, and then uses data from those video images to build a detailed model of how the animal moves. This allows scientists to use movement and behavior as a window into brain function.
Duke AI Health’s Ursula Rogers Presents at AMIA
Ursula Rogers, senior informaticist with Duke Forge and AI Health, recently presented a poster at the American Medical Informatics Association (AMIA) 2021 Virtual Informatics Summit. The poster, “Enabling Data Liquidity for Health Data Science: A Suite of APIs for EHR Data” discusses an ongoing partnership between the Duke Health Technology Solutions (DHTS) Analytic Center of Excellence and AI Health. 18 application programming interfaces (API) have been developed to provide efficient and secure programmatic access to electronic health record (EHR) data for machine learning.
Duke +DS Student Spotlight: Harshavardhan Srijay at the AMIA 2021 Virtual Informatics Summit
Since it was declared a global pandemic in March 2020, COVID-19 upturned university and college campuses across the United States, causing major disruption to student life. As Duke’s campus went into a full lockdown following a steep uptick in COVID-19 infections in North Carolina last spring, Duke’s Harshavardhan (Harsha) Srijay, a 19-year-old second-year undergrad student majoring in math and data science, saw his plans for the 2020 summer crumble. As prior opportunities fell through the cracks, the Duke Plus Data Science (+DS) Advanced Projects summer program provided him a platform to not only be engaged and productive through a very difficult summer, but also come out of it with a successful project that he recently presented at the American Medical Informatics Association (AMIA) 2021 Virtual Informatics Summit(link is external).
Duke AI Health Proposal Studio on -Omics Projects | April 5
In this one-hour virtual learning experience, 3 teams of Duke investigators will discuss their proposal concepts with data science experts. For April 5, proposal concepts will include genomic analysis related to sickle cell anemia, lifestyle intervention adherence, and transplant optimization. The proposal studio vLE concept is newly launching in spring 2021, with the goal of assisting Duke investigators with proposal development in health data science, and in sharing experiences with the broader Duke community. The series is co-hosted by Duke AI Health and the Duke+Data Science (+DS) program.
Now Accepting Proposals for Placement of a Pathology AI Health Fellow for Projects within the Department
AI Health is currently considering requests for placement of a Pathology AI Health Data Science Fellow. The AI Health Data Science Fellowship is a 2-year training program in data science with direct application for healthcare. The Pathology AI Health fellow will be funded jointly by AI Health and the Department of Pathology. Fellows will also receive support from AI Health and the Duke Department of Biostatistics and Bioinformatics, with overall program supervision provided by the Duke Clinical Research Institute’s Center for Predictive Medicine.
Now Accepting Proposals for Placement of a Microsoft–Duke AI Health Fellow for Projects within the School of Medicine
AI Health is currently considering requests for placement of a Microsoft-Duke AI Health Data Science Fellow for projects proposed by Departments/Divisions within the Duke University School of Medicine. The Microsoft–Duke AI Health Data Science Fellowship is a 2-year training program in data science with direct application for healthcare. Funded in part by a grant from the Microsoft Corporation, Microsoft-Duke AI Health Fellows will also receive support from AI Health, the clinical divisions to whose projects they are assigned, and the Duke Department of Biostatistics and Bioinformatics, with overall program supervision provided by the Duke Clinical Research Institute’s Center for Predictive Medicine.
Call for Applications: The AI Health Proposal Studios | November 11, 2020
The mission of Duke AI Health is to enable discovery, development, and implementation of artificial intelligence (AI) at Duke and beyond. A key component to achieving this goal is to foster high-impact, rigorous, and competitive proposals for scientific awards. The AI Health Proposal Studios will provide a structured opportunity for investigators to engage with Duke’s top data science expertise and thought leadership, and to receive review and feedback of the scientific components of their proposals. The deadline for submitting applications is 5:00 PM Eastern time on Monday, December 7, 2020.
Successful COVID + Data Science Seminar Series Held in Summer 2020
The COVID-19 pandemic has produced a staggering array of challenges that clinicians, public health experts, and policy makers are struggling to meet. Data scientists and quantitative experts across the globe have gone into overdrive as they work to analyze a flood of information, seeking not only to better understand, track, and predict the disease, but also to help guide the response to it and ensure that timely, accurate, and trustworthy information is readily available for everyone from scientists and clinicians to communities and members of the public.
This urgent need to bridge the worlds of data science, clinical research, and public health was the driving force behind this summer’s COVID + Data Science Virtual Seminars. Sponsored by Duke Plus Data Science (+DS), the 8-week series in summer 2020 was devoted to exploring data science methods with direct applications to the COVID-19 pandemic.
The series of 12 lectures attracted more than 1,500 virtual attendances, with many participants joining across multiple weeks and topics. The series, which wrapped up in late August, provided the opportunity for audiences both within Duke and beyond to hear directly from experts on topics spanning data analysis and visualization, deep learning, statistical methods, natural language processing, molecular methodology, and more.
Read the full story at the Center for Computational Thinking website: https://computationalthinking.duke.edu/2020/11/03/plus-ds-covid-2020-summer-seminars/
Machine Learning for Mobile Health Workshop Invites Abstracts
A Machine Learning for Mobile Health workshop, part of the upcoming Neural Information Processing Systems Conference (NeurIPS 2020), is inviting contributions and extended abstracts from researchers and clinicians in the interdisciplinary machine learning and mobile health space, with the goal to better address the various challenges currently facing the widespread use of mobile health technologies in health and healthcare. Co-organized by Duke Statistical Science assistant professor Katherine Heller, PhD who is also a research scientist at Google AI, the workshop aims to facilitate collaboration between machine learning researchers, statisticians, mobile sensing researchers, human-computer interaction researchers, and clinicians from around the world.
Duke +DS Upcoming Virtual Learning Experiences (vLEs)
Seven Duke +DS learning experiences will be held in September. These sessions offer the opportunity to dive deeper into topics and target diverse units at Duke: from those that desire a broad understanding of what is possible with data science, and those who wish to use data-science tools (software) without a need for deep understanding of underlying methodology, to those who desire a rigorous technical proficiency of the details and methodology of data science. Anyone in the Duke community is welcome to join, there is no fee to attend, and no prior experience is necessary.
Duke DataFest Analysis Supports Effectiveness of Social Distancing in Reducing the Spread of COVID-19
Across the world and in the United States, multiple studies have shown that social distancing is effective at reducing the spread of SARS-CoV-2 both at interpersonal and statewide levels. An early analysis of social distancing in the United States amid the COVID-19 pandemic, presented at this year’s Duke American Statistical Association (ASA) DataFest: COVID-19 Virtual Data Challenge by Duke undergraduates Shannon Houser and Jack Lichtenstein, echoed those findings and won the “Best Visualizations” prize at the contest. Using data available from Google Mobility Reports, the duo explored how factors such as population density, initial number of positive coronavirus cases per capita, governor’s political affiliation, and official shelter-in-place orders influenced the magnitude of a state’s social distancing early during the COVID-19 pandemic.
Duke DataFest Analysis Reveals How COVID-19 Impacts Communities Already Suffering from Health Disparities
Aside from altering the very fabric of daily life across the United States and the world, the COVID-19 pandemic has exposed the many existing shortcomings and inequities of the American healthcare system. The burgeoning public health crisis has resulted in more than 5 million confirmed cases nationwide and close to 163,000 deaths as of the beginning of August. However, some communities and groups have been disproportionately impacted, as a prize-winning analysis by Duke’s Meredith Brown, Matt Feder, and Pouya Mohammadi, presented at this year’s Duke American Statistical Association (ASA) DataFest: COVID-19 Virtual Data Challenge.
Duke Hosts Symposium on Addressing Public Health Crises with Data Science
The Duke University School of Medicine’s Office of Data Science and Information Technology and Duke AI Health partnered to co-host a virtual symposium on Wednesday, June 24, focused on using data science to combat public health crises. In her opening remarks, School of Medicine Dean Mary E. Klotman said that we are in the midst of two pandemics—COVID-19 and racism. The School of Medicine, of which the DCRI is part, will play an active role in driving solutions to both of these pandemics, and data science is one of the key tools that will be used. The symposium, titled “Public Health Crises of 2020: Battling COVID-19 and Disparities with Data,” featured seven DCRI faculty and data science experts from a range of other Duke entities such as Duke Forge and AI Health. The event also featured speakers external to the University, including two keynote speakers from the NC Department of Health and Human Services, as well as speakers from Change Healthcare and Amazon Web Services Data Exchange. The event, which was delivered in rapid-fire five-minute talks, was hosted and moderated by the DCRI’s Michael Pencina, PhD, Vice Dean for Data Science & Information Technology (pictured left). Other DCRI speakers included Jessilyn Dunn, PhD; Benjamin Goldstein, PhD; Ricardo Henao, PhD; Keith Marsolo, PhD; Susanna Naggie, MD; and DCRI fellow Jedrek Wosik, MD.
Winners Announced for Duke ASA DataFest COVID-19 Virtual Data Challenge
Creativity and insight were on display as a panel of judges announced the winners of the Duke ASA DataFest: COVID-19 Virtual Data Challenge on May 5th. The contest, which took place from April 8th through April 22nd, encouraged Duke students to use data science to explore unique effects of the COVID-19 pandemic on daily life and different aspects of the social fabric of the United States. Contest participants, working alone or in teams, were prompted to use publicly available data resources to gain insights into the cultural and societal impact of the global COVID-19 pandemic. The entries were judged by a panel of 15 experts drawn from academia and industry, with prizes awarded in categories that included “Most creative topic or data set”; “Best Visualizations”; “Best Interactive Dashboard”; “Best Insight”; and a “Judges’ Pick” award to recognize achievement outside of the other categories.
A Primer on Biodefense Data Science and Technology for Pandemic Preparedness
BLOG: “During the onset of an event such as the one we’re now experiencing, resilience is the key priority. Secure your systems and protect your family and business. Remember, cybercrime spreads just as easily from personal devices to work devices as viruses do between people. Biodefense may have previously been considered the domain of the military and antiterrorism experts, but all of us now have a potential role to play. Please consider lending your time and expertise.” – Eric D. Perakslis, PhD
Learning by Doing with Electronic Health Record Data
BLOG: “As we advance into the era of learning health systems, we need to systematize a process for how clinicians and data scientists can work together to solve important problems with EHR data.” – Andrew Olson, MPP, and Scott Kollins, PhD
Duke Biomedical Engineers Find Heart Rate Measurements of Wearable Monitors Vary by Activity, Not Skin Color
Biomedical engineers at Duke University have demonstrated that while different wearable technologies, like smart watches and fitness trackers, can accurately measure heart rate across a variety of skin tones, the accuracy between devices begins to vary wildly when they measure heart rate during different types of everyday activities. The study results appear online on February 10 in the journal NPJ Digital Medicine.
Funding Opportunity: Research in Health Data Science
In collaboration with Duke AI Health and Duke Forge, the Duke Department of Medicine is seeking proposals for research in health data science. This request for proposals is designed to fulfill two missions: 1) to grow Department of Medicine faculty involvement in health data science; and 2) to support research that will then be used to improve the quality of care for patients at Duke Health.
The Department is particularly interested in proposals that utilize data from the Duke University Health System. We plan to fund several 1-year awards.
The deadline for submitting proposals has been extended to 5:00 PM Eastern time, Thursday, April 30th, 2020.
Now Accepting Applications for 2020 Health Data Science Postdoctoral Scholar
Duke Forge, in collaboration with Duke AI Health, announces a new scholarship opportunity for research at the intersection of quantitative science and health. This program is open to postdoctoral researchers in quantitative science programs. This program funds innovative, strategic, and creative researchers to develop and apply new analytical tools to solve challenges in human health and the delivery of healthcare. Successful applicants will show evidence of outstanding research ability and strong interest in health data science.
Introducing AI Health
Duke Vice President for Research Lawrence Carin, PhD, and Forge Co-Director Erich Huang, MD, PhD, recently introduced AI Health, Duke’s new multidisciplinary, campus-spanning organization dedicated to enabling research into applications of artificial intelligence and machine learning in healthcare, and to effectively translating that research into techniques and technologies that will improve health outcomes for patients and communities. AI Health will also have a strong presence in education and workforce development as it builds training programs to equip students, quantitative scientists, and clinicians for a future that will increasingly be shaped by data science.
+DS Student Showcase on December 4
The +DS program held a student showcase on Wednesday, December 4, 2019 from 4:30-6:00 PM in the Energy Hub Atrium (first floor of Gross Hall). More than 30 student posters were presented.
27 teams with 62 students presented from the +DS fall 2019 course POE 190.01/POE 790.01 “Introduction to Machine Learning Methods and Practice.” This mini-class has introduced students to machine learning methods that have become increasingly useful in practice, specifically deep learning and neural networks. The student have produced their end-of-semester projects with applications of machine learning to a problem of relevance to their field of study or major.
5 teams with 8 students presented from the +DS Advanced Projects. Students who have previously completed the +DS curriculum are eligible to apply for the Advanced Projects, typically structured as an independent study project, where they are mentored by experts in data science partnered within clinicians in areas of focus including dermatology, ophthalmology, pathology, radiology, and cardiology.
These +DS project-based learning teams offer Duke students, both undergraduate and graduate, the opportunity to be a part of teams applying advanced data science methods and machine learning to real-world problems, and as a means of learning the important field of machine learning in a manner that is accessible and adaptive to all Duke students.
Data Science Showcase Highlights Duke Student Experiences
Students, faculty, and staff from across Duke recently assembled for the Data Science Student Showcase, held at the Gross Hall Atrium on the morning of April 25th. Put together by the +DS Projects in Medicine and the DCRI-Forge HDS Internship Program(link is external), the event served as a platform for students to present the projects that they have been immersed in during the spring semester.
“The students have an important opportunity to learn through these experiences,” said Lisa Wruck, PhD, director of the Center for Predictive Medicine, who delivered the introductory remarks at the event. “We appreciate the contributions of the mentors, program coordinators, and the hard work of the students themselves.”
The Showcase featured the efforts of nine students with the DCRI-Forge HDS Internship Program. Through brief “lightning talks,” they were able to highlight the methods and initial findings of their research. Twelve student teams with the +DS Projects in Medicine also presented posters about their applied projects, for a total of 71 student participants.
“It’s not just applying machine learning models, but also about having a more immersive experience with real data, provenance, and applications,” said Ricardo Henao, PhD, Duke Forge principal data scientist and assistant professor in the Department of Biostatistics and Bioinformatics, who provided the closing remarks. “With +DS, we establish a solid background in machine learning. Then with the HDS interns, we are addressing real problems and developing the pipeline.”
The +DS Projects in Medicine is an eight-month program where students get a chance to apply state-of-the-art deep learning technology to image analysis, with the goal of assisting clinicians in making decisions on diagnosis and delivery of care. During the fall 2018 semester, students received training in deep learning with a particular focus on image analysis, constituted via the online +DS learning modules and through complementary in-person learning experiences. Thirteen student teams were formed in the spring of 2019, mentored by leading Duke Faculty involved in data science research with areas of focus in dermatology, ophthalmology, pathology, radiology, and cardiology.
The HDS Internship Program is a partnership between the Duke Clinical Research Institute (DCRI) and Duke Forge. The projects that the interns are introduced to through the internship are part of the Forge Demonstration Program in which transdisciplinary teams use advanced data science methods to “demonstrate the art of the possible.” The HDS internship program is structured as a 17-month program with interns working under the direction of quantitative experts, paired with biostatistician staff mentors primarily from the Center for Predictive Medicine, and receiving dedicated technical and professional skills training.
“Our goal is to offer Duke students, both undergraduate and graduate, the important opportunity to be a part of research teams,” said Larry Carin, PhD, Duke University Vice Provost for Research.